1. Field of the Invention
This invention relates generally to the field of pattern searching and recognition. More particularly, this invention relates to use of a combination of neural networks with correlation techniques to quickly and accurately recognize patterns with reduced computational complexity.
2. Background of the Invention
Pattern Searching is one of the most extensively used functions in industrial image systems. Applications for pattern searching can be generally divided into three categories: alignment, gauging, and inspection. Conventional pattern searching basically involves two steps of operations. First comes the problem of locating one or more regions of interest in a larger image, then isolating the patterns in the regions of interest for the next step of the process. For example, in circuit board alignment applications, the second step is to find the exact locations of the pre-defined fiducial marks and then align the circuit board to a reference based on the positions of those found fiducial marks.
In the past few years, neural networks have been quite successfully applied to many pattern recognition and vision problems. Several such applications are described in Fukushima, K., "Neocognition: A hierarchical neural network capable of visual pattern recognition," Neural Networks 1, 119-130, 1988; Rumelhart D. E., Hinton, G. E., and Williams, R. J., "Leaning internal representation by back-propagating errors," Nature, 323:533-536, 1989; and LeCunn, Y. and et al, "Backpropagation Applied to Handwritten Zip Code Recognition," Neural Computation 1, 541-551, 1989. However, most of the applications in these areas have been using binary images, for example character recognition. To accurately locate patterns, in general, gray-level images are required. Due to the variations in brightness in gray-level images, it is difficult to train neural networks to accurately locate patterns in different lighting conditions.
On the other hand, the correlation method which measures the geometric similarity between an image and reference models has been widely used in many industrial image systems. One of the most important properties in correlation is that correlation is independent of uniform linear changes in brightness in either the models and images. However, correlation itself is a very computationally expensive operation. Moreover, to search patterns, correlation has to be applied to all possible locations in the regions that may contain the patterns. For example, to search an 8.times.8 pattern in a 16.times.16 image, 81 correlations are required to find the best correlation between the pattern and model. Therefore, special hardware is usually required as described in "Technical Description: Vision for Industry", Cognex Corporation, 1990.
The present invention seeks to ameliorate some of these problems by using the speed of neural network techniques to complement the accuracy and grey scale handling abilities of correlation techniques.